Tag
diffusion-models
- Jul 16, 2026 Denoising Score Matching and the Noise-Prediction Objective How regressing on the noise that was added to a sample yields an estimate of the data score, and why the equivalence holds without any normalising constant. deep learning
- Jul 3, 2026 Forward Diffusion as Closed-Form Gaussian Corruption How a fixed Markov chain turns any sample into noise in a fixed number of steps, why the cumulative transform is Gaussian in closed form, and what that buys for training. deep learning
- Feb 15, 2026 Latent Diffusion and the Cost of Compressing Before Generating How moving the diffusion process into a compressed latent space changes the compute budget, what the autoencoder bottleneck removes, and which losses it silently introduces. deep learning
- Sep 6, 2024 Classifier-Free Guidance as an Interpolation of Two Predictions How a single conditional model can be pushed toward its conditional distribution by extrapolating away from its unconditional prediction, and what that costs. deep learning
- Aug 6, 2024 Noise Schedule Shape and the Signal-to-Noise Ratio Curve Why the choice of β schedule changes where a diffusion model spends its capacity, how variance-preserving and variance-exploding conventions differ, and when a cosine schedule beats a linear one. deep learning